Part of the work performed during development of this invention utilized U.S. Goverment funds. The U.S. Government has certain rights in this invention.
1. Field of the Invention
The invention described herein relates to assessment of medical conditions, and more particularly to the assessment of neurological conditions through statistical methods.
2. Background Art
It is well known that neurological anomalies can be reflected in the electrical activity of the brain. Such electrical activity is therefore commonly used to diagnose a variety of neurological disorders or to evaluate the treatment thereof. Electrical activity in the brain is typically captured and analyzed in the form of an electroencephalograph (EEG).
Neurological diagnosis using EEGs has a number of drawbacks, however. An EEG, when viewed as a waveform, can only be analyzed with respect to frequency and power. An EEG cannot be analyzed in the time domain given that an EEG represents brain activity uncorrelated to any particular event in time. Moreover, EEGs tend to have significant variance over multiple trials even when performed on the same individual. This is due, in part, to the tendency of patients to react to ambient stimuli while the EEGs are being taken. In addition, EEGs, as signals, tend to have a low signal to noise ratio (SNR). Once collected, EEGs are difficult to analyze because of these factors. Analysis of EEGs by medical personnel tends to be difficult and time consuming.
Because of the difficulties in performing EEG analysis, the use of evoked response potentials (ERPs) has been proposed. An ERP represents neural electrical activity that occurs as a result of a specific sensory stimulus to the patient, such as a flash of light or a tone. The electrical activity, measured as voltage (that is, potential), is therefore an evoked response to a stimulus. Like an EEG, an ERP is typically collected and analyzed as a waveform. Unlike an EEG, however, an ERP can be analyzed in the time domain as well as the frequency domain. ERPs also tend to be less variable than EEGs over multiple trials on a given patient. Nonetheless, as in the case of EEGs, artifacts occur in ERPs and their removal is difficult and error-prone. Noise reduction is also difficult. The use of ERPs as a diagnostic tool is thus expensive and time consuming. Attempts to automate ERP processing have not been widely successful.
Hence there is a need for a method of assessing the neurological condition of a patient, where the method obtains and processes relatively consistent, low noise, artifact-free data. Moreover, the diagnostic method must be fast, inexpensive, and reliable.
The invention described herein provides a method of diagnosing the presence of a neurological disorder (such as Alzheimer""s Disease, depression, or schizophrenia), otherwise assessing the neurological condition of a patient, or characterizing the results of a treatment regimen used by a patient. The method includes the collection and analysis of ERP data. The method of the invention begins by conducting a plurality of ERP trials on a patient. In an embodiment of the invention, the data from the ERP trials is then characterized to produce a characterizing ERP signal vector for the patient. This reduces the artifacts and noise level in the data. Projections based on the characterizing ERP signal vector are then generated. The projections are compared to information derived from the ERP data of patients having known neurological conditions. To perform diagnosis, for example, the projections are compared to standards, such as one or more characterizing ERP signal vectors from known healthy patients, and one or more characterizing ERP signal vectors from patients known to have the disorder. The probable presence or absence of the neurological disorder is decided by a weighted vote of the projections, where the weighting is a function of how closely each projection compares to the respective standards. Projections can also be used to perform other types of neurological assessment, such as tracking a patient""s response to a treatment regimen, assessing the treatability of a patient with respect to a particular regimen, determining the effects of a particular regimen, or categorizing a patient in order to create a homogenous group for a clinical trial.
The foregoing and other features and advantages of the invention will be apparent from the following, more particular description of a preferred embodiment of the invention, as illustrated in the accompanying drawings.
FIG. 1 is a flowchart illustrating the diagnosis process, according to an embodiment of the invention.
FIG. 2 is a flowchart illustrating the step of characterizing ERP data, according to an embodiment of the invention.
FIG. 3 illustrates the process of concatenating waveforms that represent ERP signals collected by different electrodes, according to an embodiment of the invention.
FIG. 4 illustrates the process of sampling a concatenated signal to produce a vector of amplitude values, according to an embodiment of the invention.
FIG. 5 is a flowchart illustrating the step of conducting a weighted vote to facilitate a diagnosis, according to an embodiment of the invention.
FIG. 6 is a flowchart illustrating the process for assessing the treatability of a patient, according to an embodiment of the invention.
FIG. 7 is a flowchart illustrating the process for assessing the response of a patient to a treatment regimen, according to an embodiment of the invention.
FIG. 8 is a flowchart illustrating the process for assessing the nature and extent of side effects resulting from a treatment regimen, according to an embodiment of the invention.
FIG. 9 is a flowchart illustrating the process for characterizing the results of a treatment regimen.
FIG. 10 is a flowchart illustrating the process for using ERP signals to identify candidates that can be excluded from a test population.
FIG. 11 illustrates an exemplary plot of candidates in a multidimensional information space.
FIG. 12 is a flowchart illustrating a generalized method of an embodiment of the invention.
FIG. 13 illustrates an exemplary computer system that executes a software embodiment of the invention.